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Artifact Reduction

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Mainak Biswas – One of the best experts on this subject based on the ideXlab platform.

  • support vector machine svm based compression Artifact Reduction technique
    Journal of The Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram

    Abstract:

    — A compression ArtifactReduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to ArtifactReduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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  • Support Vector Machine (SVM) based compression ArtifactReduction technique
    Journal of the Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram

    Abstract:

    — A compression ArtifactReduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to ArtifactReduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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  • compression Artifact Reduction using support vector regression
    International Conference on Image Processing, 2006
    Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak Biswas

    Abstract:

    In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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Sanjeev Kumar – One of the best experts on this subject based on the ideXlab platform.

  • support vector machine svm based compression Artifact Reduction technique
    Journal of The Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram

    Abstract:

    — A compression ArtifactReduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to ArtifactReduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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  • Support Vector Machine (SVM) based compression ArtifactReduction technique
    Journal of the Society for Information Display, 2007
    Co-Authors: Mainak Biswas, Sanjeev Kumar, T Q Nguyen, Nikhil Balram

    Abstract:

    — A compression ArtifactReduction algorithm based on support vector regression is proposed. The algorithm belongs to a broad family of standard reconstruction methods, but a standardization model is determined from a set of training samples of original images and the corresponding noise-corrupted version. As opposed to ArtifactReduction methods specific to each type of compression Artifact (e.g., blocking, ringing, etc.), we treat such Artifacts as a manifestation of the same problem, which is the quantization of DCT coefficients. In the testing step, the algorithm tries to undo the effect of quantization by using the relationship between the original and Artifact-corrupted image, determined during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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  • compression Artifact Reduction using support vector regression
    International Conference on Image Processing, 2006
    Co-Authors: Sanjeev Kumar, T Q Nguyen, Mainak Biswas

    Abstract:

    In this paper, we propose a compression Artifact Reduction algorithm based on v support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to Artifact Reduction methods specific to each type of compression Artifact (e.g. blocking, ringing etc), we treat such different Artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and Artifact-corrupted image) learned during the training step. Experimental results exhibit significant Reduction in all types of compression Artifacts.

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Naveen Subhas – One of the best experts on this subject based on the ideXlab platform.

  • combined dual energy and single energy metal Artifact Reduction techniques versus single energy techniques alone for lesion detection near an arthroplasty
    American Journal of Roentgenology, 2020
    Co-Authors: Suraj Chandrasekar, Nancy A Obuchowski, Andrew N Primak, Ceylan Colak, Wadih Karim, Naveen Subhas

    Abstract:

    OBJECTIVE. The purpose of this study was to compare a combined dual-energy CT (DECT) and single-energy CT (SECT) metal Artifact Reduction technique with a SECT metal Artifact Reduction technique fo…

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  • imaging of arthroplasties improved image quality and lesion detection with iterative metal Artifact Reduction a new ct metal Artifact Reduction technique
    American Journal of Roentgenology, 2016
    Co-Authors: Naveen Subhas, Joshua M Polster, Nancy A Obuchowski, Andrew N Primak, F Dong, Brian R Herts, Joseph P Iannotti

    Abstract:

    OBJECTIVE. The purpose of this study was to compare iterative metal Artifact Reduction (iMAR), a new single-energy metal Artifact Reduction technique, with filtered back projection (FBP) in terms of attenuation values, qualitative image quality, and streak Artifacts near shoulder and hip arthroplasties and observer ability with these techniques to detect pathologic lesions near an arthroplasty in a phantom model. MATERIALS AND METHODS. Preoperative and postoperative CT scans of 40 shoulder and 21 hip arthroplasties were reviewed. All postoperative scans were obtained using the same technique (140 kVp, 300 quality reference mAs, 128 × 0.6 mm detector collimation) on one of three CT scanners and reconstructed with FBP and iMAR. The attenuation differences in bones and soft tissues between preoperative and postoperative scans at the same location were compared; image quality and streak Artifact for both reconstructions were qualitatively graded by two blinded readers. Observer ability and confidence to detec…

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  • Iterative metal Artifact Reduction: evaluation and optimization of technique.
    Skeletal radiology, 2014
    Co-Authors: Naveen Subhas, Andreas Krauss, Joshua M Polster, Nancy A Obuchowski, Andrew N Primak, Amit Gupta, Joseph P Iannotti

    Abstract:

    Objective
    Iterative metal Artifact Reduction (IMAR) is a sinogram inpainting technique that incorporates high-frequency data from standard weighted filtered back projection (WFBP) reconstructions to reduce metal Artifact on computed tomography (CT). This study was designed to compare the image quality of IMAR and WFBP in total shoulder arthroplasties (TSA); determine the optimal amount of WFBP high-frequency data needed for IMAR; and compare image quality of the standard 3D technique with that of a faster 2D technique.

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